In [1]:
# import the necessary Python libraries and the dataset

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

data = pd.read_csv("apple_products.csv")
data.head()
Out[1]:
Product Name Product URL Brand Sale Price Mrp Discount Percentage Number Of Ratings Number Of Reviews Upc Star Rating Ram
0 APPLE iPhone 8 Plus (Gold, 64 GB) https://www.flipkart.com/apple-iphone-8-plus-g... Apple 49900 49900 0 3431 356 MOBEXRGV7EHHTGUH 4.6 2 GB
1 APPLE iPhone 8 Plus (Space Grey, 256 GB) https://www.flipkart.com/apple-iphone-8-plus-s... Apple 84900 84900 0 3431 356 MOBEXRGVAC6TJT4F 4.6 2 GB
2 APPLE iPhone 8 Plus (Silver, 256 GB) https://www.flipkart.com/apple-iphone-8-plus-s... Apple 84900 84900 0 3431 356 MOBEXRGVGETABXWZ 4.6 2 GB
3 APPLE iPhone 8 (Silver, 256 GB) https://www.flipkart.com/apple-iphone-8-silver... Apple 77000 77000 0 11202 794 MOBEXRGVMZWUHCBA 4.5 2 GB
4 APPLE iPhone 8 (Gold, 256 GB) https://www.flipkart.com/apple-iphone-8-gold-2... Apple 77000 77000 0 11202 794 MOBEXRGVPK7PFEJZ 4.5 2 GB
In [2]:
#checking for null values:

data.isnull().sum()
Out[2]:
Product Name           0
Product URL            0
Brand                  0
Sale Price             0
Mrp                    0
Discount Percentage    0
Number Of Ratings      0
Number Of Reviews      0
Upc                    0
Star Rating            0
Ram                    0
dtype: int64
In [3]:
# descriptive statistics of the data:

data.describe()
Out[3]:
Sale Price Mrp Discount Percentage Number Of Ratings Number Of Reviews Star Rating
count 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000
mean 80073.887097 88058.064516 9.951613 22420.403226 1861.677419 4.575806
std 34310.446132 34728.825597 7.608079 33768.589550 2855.883830 0.059190
min 29999.000000 39900.000000 0.000000 542.000000 42.000000 4.500000
25% 49900.000000 54900.000000 6.000000 740.000000 64.000000 4.500000
50% 75900.000000 79900.000000 10.000000 2101.000000 180.000000 4.600000
75% 117100.000000 120950.000000 14.000000 43470.000000 3331.000000 4.600000
max 140900.000000 149900.000000 29.000000 95909.000000 8161.000000 4.700000
In [4]:
# Now I will create a new dataframe by storing all the data about the top 10 highest-rated iPhones in India on Flipkart. 
# It will help in understanding what kind of iPhones are liked the most in India:

highest_rated = data.sort_values(by=["Star Rating"], ascending=False)
highest_rated = highest_rated.head(10)
highest_rated["Product Name"]
Out[4]:
20     APPLE iPhone 11 Pro Max (Midnight Green, 64 GB)
17         APPLE iPhone 11 Pro Max (Space Grey, 64 GB)
16    APPLE iPhone 11 Pro Max (Midnight Green, 256 GB)
15               APPLE iPhone 11 Pro Max (Gold, 64 GB)
14              APPLE iPhone 11 Pro Max (Gold, 256 GB)
0                    APPLE iPhone 8 Plus (Gold, 64 GB)
29                     APPLE iPhone 12 (White, 128 GB)
32          APPLE iPhone 12 Pro Max (Graphite, 128 GB)
35                     APPLE iPhone 12 (Black, 128 GB)
36                      APPLE iPhone 12 (Blue, 128 GB)
Name: Product Name, dtype: object
In [5]:
# Now let’s have a look at the number of ratings of the highest-rated iPhones on Flipkart:

iphones = highest_rated["Product Name"].value_counts()
label = iphones.index
counts = highest_rated["Number Of Ratings"]
figure = px.bar(highest_rated, x=label, 
                y = counts, 
            title="Number of Ratings of Highest Rated iPhones")
figure.show()
In [6]:
# Now let’s have a look at the number of reviews of the highest-rated iPhones on Flipkart:

iphones = highest_rated["Product Name"].value_counts()
label = iphones.index
counts = highest_rated["Number Of Reviews"]
figure = px.bar(highest_rated, x=label, 
                y = counts, 
            title="Number of Reviews of Highest Rated iPhones")
figure.show()
In [7]:
# Now let’s have a look at the relationship between the sale price of iPhones and their ratings on Flipkart:

figure = px.scatter(data_frame = data, x="Number Of Ratings",
                    y="Sale Price", size="Discount Percentage", 
                    trendline="ols", 
                    title="Relationship between Sale Price and Number of Ratings of iPhones")
figure.show()
In [8]:
# Now let’s have a look at the relationship between the discount percentage on iPhones on Flipkart and the number of ratings:

figure = px.scatter(data_frame = data, x="Number Of Ratings",
                    y="Discount Percentage", size="Sale Price", 
                    trendline="ols", 
                    title="Relationship between Discount Percentage and Number of Ratings of iPhones")
figure.show()
In [ ]: